In manufacturing technology, the Philippines has been left behind by its East Asian neighbors because of policy mistakes in the past.Although there are signs that manufacturing is going through a renaissance in the Philippines during the past few years, especially fueled by domestic demand, it will take some time before we can catch up with our peers, even in Southeast Asia alone.In a recent workshop on Big Data held at the University of Asia and the Pacific, there prevailed an optimistic view that, given close cooperation among the Government, academe and business sector, the Philippines can be at the forefront of Big Data Analysis as we try to upgrade the BPO-IT sector from voice-oriented BPO to KPO (knowledge process outsourcing).One of the movers in this fledgling industry is former Secretary of Trade and Industry, Gregorio Domingo.As a key investment and industry adviser in the large conglomerate of the SM Group, Mr. Domingo is setting his sights on significantly increasing the use of data analytics in the operations of the leading retailer in the country that has diversified into real estate, logistics, banking, education, energy and many other sectors.Just consider all the data that are being generated by the numerous retailing outlets in the SM malls and the banking transactions of BDO, the largest bank in the country.Harnessing these data for better decision making can significantly increase the productivity of their business operations.

A Management Engineering graduate from the Ateneo University, Secretary Domingo has the necessary quantitative skills to dialogue with the key players in data analytics. He already has set up a data analytics group within the SM Investments Corporation which he heads. Responding to questions posed by some academics planning to put up graduate training programs for experts in data analytics, he stressed that the data science course should be at the masteral level and not an undergraduate course. Those who have the ideal preparation to pursue a possible masteral program in data analytics are professionals with undergraduate degrees in engineering, mathematics, information technology, computer science and the like. He broke down the skills required of a data analyst into four levels: The most basic are the tabular skills (Excel, etc.). The next level is visualization. At present, most of our data analysts are at these two levels, which can be described as descriptive. He said that people who talk about Artificial Intelligence (AI) limit themselves to neural networks. The applications for AI using neural networks are few. Data analytics in the Philippines at present is basically descriptive because we do not have enough talent to move up to the prescriptive and predictive levels.

Practitioners from the industry described ongoing efforts to increase the supply of talents in data analytics. Mr. Sherwin Pelayo of Pointwest Innovations Corporation reported that his organization identified three levels in crafting competencies in this field. They recognized that it was not easy for one person to have all the required competencies at the higher levels. So they developed specializations by stages. There are the data stewards who know the data and create the data. At a higher level are the data engineers who collect the data as data architects. The data scientists are the ones who do the visualizations, models, algorithms. Their organization is building specific career paths, cognizant of the reality that not all the competencies can reside in one person. Some of the workers who can be retrained along these specializations are the more qualified professionals among the more than one million employees in the BPO-IT sector that now generates more than $25 billion of revenues annually. Ms. Charlene Chan of the Information Technology and Business Process Association of the Philippines (IBPAP) pointed out that the majority of the current workers in the industry have low-level skills and have to be upgraded and re-skilled. They ordinarily do not have the training for data analysis. Unless provided with further training, BPO workers are stuck with descriptive analytics. Only those workers with strong quantitative skills, especially in higher mathematics, or have the aptitude for receiving further training in quantitative tools will be retrainable for the prescriptive and predictive levels of data analytics. To increase the pool of these trainable workers, it is important that more and more undergraduate specializations, even those in business administration, social sciences and the humanities are given the opportunity to take elective courses in such higher mathematics courses as integral and differential calculus and matrix algebra. As an educator, I have been advocating for some time now that Calculus be considered an important component of liberal arts education.

Dr. Brenda Quismoro, who is at the helm of the data analytics program of the University of Asia and the Pacific, clearly points out that any undergraduate program purporting to produce data analysts must incorporate a heavy dose of the liberal arts so that they develop the soft skills that are necessary for extracting meaning from both structured and unstructured data. The APEC Data Science and Analysis Competencies, an initiative led by the United States Department of Labor under APEC’s Human Resources Development Working Group and endorsed by the APEC Business Advisory Council, enumerated these skills as follows: a) problem solving skills: ability to frame the right problem; b) communication skills: articulating what is possible and explaining the work to be done and to get buy-ins; c) people skills and team work: ability to collaborate with people of various types, locations and cultures; d) ability to strike the balance between complexity and usability; d) humility: ability to see that analytics is at the service of a higher good; e) realism: ability to see limitations of data, e.g. “the future is not always in the past”; f) ethical; g) human. It is obvious that these skills go beyond the technical and can only be developed by an exposure to the interdisciplinary studies which humanities provides.

If they are to craft training programs that will address the increasing demand for data analysts, it is important that people in the Government, business and academe understand the genesis of data analytics, especially Big Data analysis over at least the last two decades. We shall provide a short backgrounder in the second part of this article. (To be continued).

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